Clustered search head configuration synchronization with bloom filter

ABSTRACT

Embodiments of the present disclosure provide techniques for efficiently and accurately performing propagation of search-head specific configuration customizations across multiple individual configuration files of search heads of a cluster for a consistent user experience. The cluster of search heads may be synchronized such that the search heads operate to receive the configuration or knowledge object customizations from one or more clients from a central or lead search head. To reduce the amount of data that is transferred during propagation, the list of configuration or knowledge object customizations maintained in each search head is filtered from the list of the lead search head until a divergence point is determined. Once determined and communicated to the lead search head, the lead search head sends the configuration and knowledge object customization data that is absent from the internal list of the member search head.

BACKGROUND

Modern data centers often comprise thousands of hosts that operatecollectively to service requests from even larger numbers of remoteclients. During operation, components of these data centers can producesignificant volumes of machine-generated data. The unstructured natureof much of this data has made it challenging to perform indexing andsearching operations because of the difficulty of applying semanticmeaning to unstructured data. As the number of hosts and clientsassociated with a data center continues to grow, processing largevolumes of machine-generated data in an intelligent manner andeffectively presenting the results of such processing continues to bepriority.

In particular, where multiple users from a single institution or evenmultiple institutions access the same data sets, maintaining aconsistent user experience across all instances and interfaces in realtime presents a distinct challenge, particularly when usercustomizations and configurations are supplied nearly simultaneously.Conventional techniques typically distribute a centralized configurationdata set among all interfaces. However, as the size of the data setscales in relation to the data being processed, distributing entire datasets can become inefficient and result in undesirable delays, potentialconflicts, and a compromised user experience.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that is further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

To reduce the amount of data is transmitted during configuration datapropagation and synchronization, embodiments of the present disclosureprovide solutions for determining the divergence between the journals ofeach member search head and the journal of the search head leader of acluster of search heads. According to one or more embodiments of thepresent disclosure, a method is provided that includes accessing a listof knowledge object customizations in a search head leader, generating areduced-size representation (e.g., a digest) of the list of knowledgeobject customizations, propagating the reduced-size representation to atleast one member search head in the cluster, and determining the pointof divergence in the member search head. In one or more embodiments, thepoint of divergence is identified by filtering the list of knowledgeobject customizations from the search head leader with a correspondinglist in the member search head and determining the point of divergence.Once the point or origin of divergence is determined for a member searchhead and reported to the search head leader, the search head leadersends the knowledge object customizations in its journal after thedivergence point to completely update that particular member searchhead. The same sequence of customization steps are also applied to allother members to ensure all member search heads in the cluster areconfigured consistently with the same knowledge customization objects.

According to a second embodiment of the present disclosure, to maintainan efficacy of the representation for the purposes of filtering, a sizeof the reduced-size representation in the search head leader ismaintained at or below a pre-determined threshold size, or the remainingcapacity of the reduced size presentation is maintained at or above athreshold capacity by comparing the size of the representation to thecorresponding thresholds, accounting for the size of an incoming update.If the size is sufficient to accommodate the incoming update, the datafrom the update can be added to the representation. Otherwise, a newrepresentation is generated and is supplied to member search heads insubsequent synchronization operations.

According to a third embodiment of the present disclosure, determinationof a point of divergence between two lists of knowledge objectcustomizations is performed by generating a Bloom filter bitmask fromthe first list, then applying one or more hash functions to the elementsin the second list to determine addresses in the bitmask. The bit valuesat the addresses are referenced and indicate the presence or absence ofeach considered element. In one or more embodiments, this evaluation isperformed for every element in the list of knowledge objectcustomizations of the member search head until an element is determinedto be absent, whereupon the element immediately preceding the absentelement is identified as the divergence point.

According to still further embodiments, to reduce the rate of falsepositives inherent in Bloom filtering, a set of variable or constantsize may be collectively evaluated, rather than each elementindividually. Where the set is of a constant size, new elements forconsideration can be iteratively appended to the end of the set, withthe top or first element being removed.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIG. 7 illustrates a user interface screen for an example datamodel-driven report generation interface in accordance with thedisclosed embodiments;

FIG. 8 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 9A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 9B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 9C illustrates a proactive monitoring tree in accordance with thedisclosed embodiments;

FIG. 9D illustrates a user interface screen displaying both log data andperformance data in accordance with the disclosed embodiments;

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system in which an embodiment may be implemented;

FIG. 11 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIG. 12 schematically illustrates an example environment for replicatinguser-defined configuration customizations across a cluster of searchheads, in accordance with the disclosed embodiments;

FIG. 13 schematically illustrates an example cluster of search heads,each including a local data store storing a journal and configurationfile, in accordance with the disclosed embodiments;

FIG. 14 presents a flowchart illustrating how a search head communicatesa configuration customization applied at a local configuration file to asearch head leader, in accordance with the disclosed embodiments;

FIG. 15 presents a flowchart illustrating how a search head synchronizesa configuration customization across a cluster of search heads, inaccordance with the disclosed embodiments;

FIG. 16 presents a flowchart illustrating how a search head resolves aconflict associated with a configuration customization, in accordancewith the disclosed embodiments;

FIG. 17 presents a flowchart illustrating how a search head leadercommunicates a specific configuration customization update to a membersearch head, in accordance with the disclosed embodiments;

FIG. 18 presents a chronological flow diagram illustrating theinteraction of components in a search cluster during a determination ofa divergence point, in accordance with the disclosed embodiments;

FIG. 19 presents a flowchart illustrating how a size of a local list ofknowledge configuration customizations in the search head leader ismanaged, in accordance with the disclosed embodiments;

FIG. 20 presents a flowchart illustrating how to iteratively identify adivergence point between the journal of a member search head and thejournal of a search head leader, in accordance with the disclosedembodiments; and

FIG. 21 presents a flowchart illustrating how to iteratively identify adivergence point between the journal of a member search head and thejournal of a search head leader with a dynamic window of contiguousconfiguration customizations, in accordance with the disclosedembodiments using

DETAILED DESCRIPTION

Embodiments are described herein according to the following outline:

-   -   1.0. General Overview        -   1.1. Overview of Configuration Replication    -   2.0. Operating Environment        -   2.1. Host Devices        -   2.2. Client Devices        -   2.3. Client Device Applications        -   2.4. Data Server System        -   2.5. Data Ingestion            -   2.5.1. Input            -   2.5.2. Parsing            -   2.5.3. Indexing        -   2.6. Query Processing        -   2.7. Field Extraction        -   2.8. Example Search Screen        -   2.9. Data Modelling        -   2.10. Acceleration Techniques            -   2.10.1. Aggregation Technique            -   2.10.2. Keyword Index            -   2.10.3. High Performance Analytics Store            -   2.10.4. Accelerating Report Generation        -   2.11. Security Features        -   2.12. Data Center Monitoring        -   2.13. Cloud-Based System Overview        -   2.14. Searching Externally Archived Data            -   2.14.1. ERP Process Features        -   2.15. IT Service Monitoring    -   3.0. Configuration Replication        -   3.1. Example Search Head Cluster Environment        -   3.2. Example Configuration Storage Environment        -   3.3. Configuration Propagation            -   3.3.1. Search Head Specific Propagation            -   3.3.2. Divergence Determination            -   3.3.3. False Positive Reduction Techniques

1.0. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5).

1.1. Overview of Configuration Replication

The present disclosure is directed to replicating knowledge objectcustomizations across multiple search heads of a cluster. Multiplesearch heads of a data aggregation and analysis system are groupedtogether to form a cluster. A search head may refer to a component ofthe data aggregation and analysis system that is responsible for areduce phase of a map-reduce search performed by the data aggregationand analysis system, as further described below. A search head maycommunicate with one or more indexers that are responsible for a mapphase of the map-reduce search, as further described below.

An example data aggregation and analysis system may aggregateheterogeneous machine-generated data received from various sources,including servers, databases, applications, networks, etc. Theaggregated source data may comprise a plurality of events. An event maybe represented by a data structure that is associated with a certainpoint in time and comprises a portion of raw machine data (i.e.,machine-generated data). The system may be configured to performreal-time indexing of the source data and to execute real-time,scheduled, or historic searches on the source data. A search query maycomprise one or more search terms specifying the search criteria. Searchterms may include keywords, phrases, Boolean expressions, regularexpressions, field names, name-value pairs, etc. The search criteria maycomprise a filter specifying relative or absolute time values, to limitthe scope of the search by a specific time value or a specific timerange.

The example data aggregation and analysis system executing a searchquery may evaluate the data relative to the search criteria to produce asearch result. The search result may comprise one or more data itemsrepresenting one or more portions of the source data that satisfy thesearch criteria. The search result that is produced by the search querycan include data derived using a late binding schema. A late bindingschema is described in greater detail below. Search results returned inresponse to search queries can be presented to users via dashboards andother graphical user interfaces (GUIs).

Users can customize the search-related behavior of the system (e.g., byspecifying interesting fields, event types and transactions, lookups andworkflow actions, etc.) and the visualization behavior of the system(e.g., how to present the search results). In some embodiments, theusers may customize the search-related and visualization behavior of thesystem by submitting a command, instruction, or request for acustomization of a knowledge object (also referred to as a “knowledgeobject customization”). A knowledge object is a configuration relatingto search activity or visualization that is permissible and controlledvia an access control layer of the system that is customizable by auser. Exemplary knowledge objects include, but are not limited to,late-binding schema, a saved search, an event type, a transaction, atag, a field extraction, a field transform, a lookup, a workflow action,a search command, and a view, which are discussed in more detail below.A customization may include any action operation relating to a knowledgeobject, such as, for example, the deletion of a knowledge object,editing of a knowledge object, sharing of a knowledge object, setting ofpermissions relating to of a knowledge object, creation of a knowledgeobject, modification of a knowledge object, changing of a knowledgeobject, or updating of a knowledge object. In operation, a user's device(“a client”) interacts with one of the search heads of the cluster tosubmit one or more commands, instructions, or requests for configurationcustomizations to the search head. In the cluster, one of the searchheads operates as a “leader” or “captain” responsible for communicatingwith the other search heads in the cluster.

The search head performs and stores the knowledge object customizationin a local data store (e.g., stored on a local disk or in memory of theindividual search head.) In an embodiment, the search head is configuredto present the stored knowledge object customizations to the clients viaa suitable user interface. In an embodiment, the search head maintains ajournal including a record of one or more knowledge objectcustomizations submitted by the clients. In response to receiving aknowledge object customization from a client, the search head adds ajournal update to the journal maintained in the local data store. Thesearch head applies the knowledge object customization by writing theknowledge object customization to a local configuration file maintainedin the local data store.

During a synchronization phase, a search head in the cluster receivesone or more knowledge object customization commits from the search headleader. The knowledge object customization commits represent knowledgeobject customizations made by other search heads in the cluster thathave been reported to and recorded by the search head leader. Inaddition, during the synchronization phase, the search head sends one ormore journal updates from its locally stored journal to the search headleader for replication to the other search heads in the cluster.Eventual consistency is achieved with respect to replication ofknowledge object customizations across the cluster of search heads, suchthat changes on one search head appear on all search heads. Accordingly,the cluster achieves a consistent overall configuration across all ofthe search heads of the cluster. In addition, the cluster is able toprovide a consistent search behavior and a consistent visualizationexperience to users, even as configurations are altered on individualmembers of the cluster.

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104.

Networks 104 broadly represent one or more LANs, WANs, cellular networks(e.g., LTE, HSPA, 3G, and other cellular technologies), or networksusing any of wired, wireless, terrestrial microwave, or satellite links,and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device or application-specific information. Monitoringcomponent 112 may be an integrated component of a client application110, a plug-in, an extension, or any other type of add-on component.Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent or received by a client application 110.For example, the monitoring component 112 may be configured to monitordata packets transmitted to or from one or more host applications 114.Incoming or outgoing data packets can be read or examined to identifynetwork data contained within the packets, for example, and otheraspects of data packets can be analyzed to determine a number of networkperformance statistics. Monitoring network traffic may enableinformation to be gathered particular to the network performanceassociated with a client application 110 or set of applications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 or client device 102. Forexample, a monitoring component 112 may be configured to collect deviceperformance information by monitoring one or more client deviceoperations, or by making calls to an operating system or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, or performing other datatransformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102 orhost devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH AFFINITY”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in Fig.) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “|” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data model oradditional fields. Data model objects that reference the subsets can bearranged in a hierarchical manner, so that child subsets of events areproper subsets of their parents. A user iteratively applies a modeldevelopment tool (not shown in Fig.) to prepare a query that defines asubset of events and assigns an object name to that subset. A childsubset is created by further limiting a query that generated a parentsubset. A late-binding schema of field extraction rules is associatedwith each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 March, 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

FIG. 7 illustrates a user interface screens where a user may selectreport generation options using data models. The report generationprocess may be driven by a predefined data model object, such as a datamodel object defined or saved via a reporting application or a datamodel object obtained from another source. A user can load a saved datamodel object using a report editor. For example, the initial searchquery and fields used to drive the report editor may be obtained from adata model object. The data model object that is used to drive a reportgeneration process may define a search and a set of fields. Upon loadingof the data model object, the report generation process may enable auser to use the fields (e.g., the fields defined by the data modelobject) to define criteria for a report (e.g., filters, splitrows/columns, aggregates, etc.) and the search may be used to identifyevents (e.g., to identify events responsive to the search) used togenerate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selection interface

Once a data model object is selected by the user, a user interfacescreen 700 shown in FIG. 7 may display an interactive listing ofautomatic field identification options 701 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 702, the “SelectedFields” option 703, or the “Coverage” option (e.g., fields with at leasta specified % of coverage) 704). If the user selects the “All Fields”option 702, all of the fields identified from the events that werereturned in response to an initial search query may be selected. Thatis, for example, all of the fields of the identified data model objectfields may be selected. If the user selects the “Selected Fields” option703, only the fields from the fields of the identified data model objectfields that are selected by the user may be used. If the user selectsthe “Coverage” option 704, only the fields of the identified data modelobject fields meeting a specified coverage criteria may be selected. Apercent coverage may refer to the percentage of events returned by theinitial search query that a given field appears in. Thus, for example,if an object dataset includes 10,000 events returned in response to aninitial search query, and the “avg_age” field appears in 854 of those10,000 events, then the “avg_age” field would have a coverage of 8.54%for that object dataset. If, for example, the user selects the“Coverage” option and specifies a coverage value of 2%, only fieldshaving a coverage value equal to or greater than 2% may be selected. Thenumber of fields corresponding to each selectable option may bedisplayed in association with each option. For example, “97” displayednext to the “All Fields” option 702 indicates that 97 fields will beselected if the “All Fields” option is selected. The “3” displayed nextto the “Selected Fields” option 703 indicates that 3 of the 97 fieldswill be selected if the “Selected Fields” option is selected. The “49”displayed next to the “Coverage” option 704 indicates that 49 of the 97fields (e.g., the 49 fields having a coverage of 2% or greater) will beselected if the “Coverage” option is selected. The number of fieldscorresponding to the “Coverage” option may be dynamically updated basedon the specified percent of coverage.

2.10. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 8 illustrates how a search query 802received from a client at a search head 210 can split into two phases,including: (1) subtasks 804 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 806 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 802, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 802 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 804, and then distributes searchquery 804 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4, or may alternativelydistribute a modified version (e.g., a more restricted version) of thesearch query to the search peers. In this example, the indexers areresponsible for producing the results and sending them to the searchhead. After the indexers return the results to the search head, thesearch head aggregates the received results 806 to form a single searchresult set. By executing the query in this manner, the systemeffectively distributes the computational operations across the indexerswhile minimizing data transfers.

2.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.11. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 9A illustrates anexample key indicators view 900 that comprises a dashboard, which candisplay a value 901, for various security-related metrics, such asmalware infections 902. It can also display a change in a metric value903, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 900 additionallydisplays a histogram panel 904 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 9B illustrates an example incident review dashboard 910 thatincludes a set of incident attribute fields 911 that, for example,enables a user to specify a time range field 912 for the displayedevents. It also includes a timeline 913 that graphically illustrates thenumber of incidents that occurred in time intervals over the selectedtime range. It additionally displays an events list 914 that enables auser to view a list of all of the notable events that match the criteriain the incident attributes fields 911. To facilitate identifyingpatterns among the notable events, each notable event can be associatedwith an urgency value (e.g., low, medium, high, critical), which isindicated in the incident review dashboard. The urgency value for adetected event can be determined based on the severity of the event andthe priority of the system component associated with the event.

2.12. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developers' task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CORRELATIONFOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OFCOMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROMTHAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Example node-expansion operations are illustrated in FIG. 9C,wherein nodes 933 and 934 are selectively expanded. Note that nodes931-939 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/253,490, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 15 Apr. 2014, and U.S. patent application Ser. No.14/812,948, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 29 Jul. 2015, each of which is hereby incorporated byreference in its entirety for all purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous datacomprising events, log data, and associated performance metrics for theselected time range. For example, the screen illustrated in FIG. 9Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 942 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316, entitled“CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCEMETRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOGDATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan.2014, and which is hereby incorporated by reference in its entirety forall purposes.

2.13. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system. Similar to the system of FIG. 2, the networkedcomputer system 1000 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system1000, one or more forwarders 204 and client devices 1002 are coupled toa cloud-based data intake and query system 1006 via one or more networks1004. Network 1004 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 1002 and forwarders204 to access the system 1006. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 1006 forfurther processing.

In an embodiment, a cloud-based data intake and query system 1006 maycomprise a plurality of system instances 1008. In general, each systeminstance 1008 may include one or more computing resources managed by aprovider of the cloud-based system 1006 made available to a particularsubscriber. The computing resources comprising a system instance 1008may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 1002 to access a web portal orother interface that enables the subscriber to configure an instance1008.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 1008) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment such as SPLUNK® ENTERPRISE and a cloud-basedenvironment such as SPLUNK CLOUD™ are centrally visible).

2.14. Searching Externally Archived Data

FIG. 11 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 1104 over network connections1120. As discussed above, the data intake and query system 108 mayreside in an enterprise location, in the cloud, etc. FIG. 11 illustratesthat multiple client devices 1104 a, 1104 b, . . . , 1104 n maycommunicate with the data intake and query system 108. The clientdevices 1104 may communicate with the data intake and query system usinga variety of connections. For example, one client device in FIG. 11 isillustrated as communicating over an Internet (Web) protocol, anotherclient device is illustrated as communicating via a command lineinterface, and another client device is illustrated as communicating viaa system developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 1104 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 1110. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 1110, 1112. FIG. 11 shows two ERP processes 1110, 1112 thatconnect to respective remote (external) virtual indices, which areindicated as a Hadoop or another system 1114 (e.g., Amazon S3, AmazonEMR, other Hadoop Compatible File Systems (HCFS), etc.) and a relationaldatabase management system (RDBMS) 1116. Other virtual indices mayinclude other file organizations and protocols, such as Structured QueryLanguage (SQL) and the like. The ellipses between the ERP processes1110, 1112 indicate optional additional ERP processes of the data intakeand query system 108. An ERP process may be a computer process that isinitiated or spawned by the search head 210 and is executed by thesearch data intake and query system 108. Alternatively or additionally,an ERP process may be a process spawned by the search head 210 on thesame or different host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 1110, 1112 receive a search request from the searchhead 210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 1110, 1112 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 1110, 1112 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 1110, 1112 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices1114, 1116, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 1104 may communicate with the data intake and querysystem 108 through a network interface 1120, e.g., one or more LANs,WANs, cellular networks, intranetworks, or internetworks using any ofwired, wireless, terrestrial microwave, satellite links, etc., and mayinclude the public Internet.

The analytics platform utilizing the External Result Provider process isdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.14.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the ]streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.15. IT Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE™, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE™ system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE™ enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE™ application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a search querythat derives a KPI value from the machine data of events associated withthe entities that provide the service. Information in the entitydefinitions may be used to identify the appropriate events at the time aKPI is defined or whenever a KPI value is being determined. The KPIvalues derived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to search query processing. AggregateKPIs may be defined to provide a measure of service performancecalculated from a set of service aspect KPI values; this aggregate mayeven be taken across defined timeframes or across multiple services. Aparticular service may have an aggregate KPI derived from substantiallyall of the aspect KPI's of the service to indicate an overall healthscore for the service.

SPLUNK® IT SERVICE INTELLIGENCE™ facilitates the production ofmeaningful aggregate KPI's through a system of KPI thresholds and statevalues. Different KPI definitions may produce values in differentranges, and so the same value may mean something very different from oneKPI definition to another. To address this, SPLUNK® IT SERVICEINTELLIGENCE™ implements a translation of individual KPI values to acommon domain of “state” values. For example, a KPI range of values maybe 1-100, or 50-275, while values in the state domain may be ‘critical,’‘warning,’ ‘normal,’ and ‘informational’. Thresholds associated with aparticular KPI definition determine ranges of values for that KPI thatcorrespond to the various state values. In one case, KPI values 95-100may be set to correspond to ‘critical’ in the state domain. KPI valuesfrom disparate KPI's can be processed uniformly once they are translatedinto the common state values using the thresholds. For example, “normal80% of the time” can be applied across various KPI's. To providemeaningful aggregate KPI's, a weighting value can be assigned to eachKPI so that its influence on the calculated aggregate KPI value isincreased or decreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE™ can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE™ can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be created and updated by an import of tabulardata (as represented in a CSV, another delimited file, or a search queryresult set). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in SPLUNK® IT SERVICE INTELLIGENCE™ can also be associatedwith a service by means of a service definition rule. Processing therule results in the matching entity definitions being associated withthe service definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE™ can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (ITSM)system, such as a systems available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE™ provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE™ provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

3.1. Search Head Cluster Environment

FIG. 12 presents a functional block diagram of a distributed system1200. The system 1200 includes a cluster 1210 of multiple search heads1204 accessible by a number of clients 1220 over a network 1201.Although FIG. 12 illustrates an example including four search heads 1204and three clients 1220, it is to be appreciated that the system 1200 mayinclude any number of search heads 1204 and any number of clients 1220.

The search heads 1204 may be, for example, server devices located at oneor more sites, each site being geographically remote from the other site(e.g., in different cities). In an embodiment, all of the search heads1204 may be located at a single site. In another embodiment, at leasttwo of the search heads 1204 are located at a single site and at leastone other of the search heads 1204 of the cluster 1210 is located at adifferent site. In another embodiment, each search head 1204 of thecluster 1210 is located at a different site.

The search heads 1204 may receive instructions to perform tasks from theclients 1220 on behalf of one or more users 1225. For example, one ofthe search heads 1204 may receive a request from one of the clients 1220to perform a search-related activity, such as a map-reduce search or asearch using late-binding schema as described in detail below. Inresponse to receiving the request to perform the search-relatedactivity, the search head 1204 may perform the search.

According to embodiments of the present disclosure, the search heads1204 may receive instructions from the clients 1220 to perform aknowledge object customization (i.e., a customization of asearch-related or visualization-related configuration). A customizationmay include any action relating to a knowledge object, such as, forexample, the deletion, creation, modification, change, or update of aknowledge object. A knowledge object is a configuration relating tosearch, visualization, or other system activity. As such, a knowledgeobject can be a configuration of an activity that is permissible orcontrolled via an access control layer of the system. A knowledge objectcan be customizable by a user.

Exemplary knowledge objects include, but are not limited to, a savedsearch, an event type, a transaction, a tag, a field extraction, a fieldtransform, a lookup, a workflow action, a search command, a view, andlate-binding schema. Although various exemplary knowledge objects aredescribed below, it is noted that other knowledge objects(search-related or visualization-related configurations) may becustomized in accordance with embodiments of the present disclosure.

In an embodiment, a view is a knowledge object representing acustomizable user interface accessible by a user 1225. In an embodiment,dashboards and forms are examples of views. In an embodiment, a view maybe built from user interface components such as search boxes, fields,charts, tables, and lists. A view may be permissible and may be appliedto specific application. A dashboard may be a static or dynamic (e.g.,real-time) visualization of data. In an embodiment, a view may definedvia user-specified HTML, “custom” XML formats, or the like.

In an embodiment, a saved search is a knowledge object representing asearch that has been made available for later use. For example, searchesmay be saved for producing a report, an alert or a dashboard panel.

In an embodiment, an event type is a knowledge object that enables auser to categorize and label all indexed events that match a specifiedsearch string (e.g., a search query or search criteria). An event typemay have a name and an associated search query or search criteria. Auser may create an event type directly or use a device to identify andcreate an event type. For example, the user may use a device such as atypelearner (i.e., a utility that helps a user create event types,examines events resulting from a search, and suggests event types), anevent type finder (i.e., a ‘findtypes’ command may be appended to asearch to cause the search results to display as a breakdown of the mostcommon groups of events found in the search results), or an event typebuilder (i.e., a utility or tool configured to dynamically create eventtypes based on an analysis of a selected event).

In an embodiment, a transaction is a knowledge object representing agroup of conceptually-related events that span time. For example, eventsgrouped together by a transaction often represent a complex, mult-istepbusiness-related activity, such as all events related to a single hotelcustomer reservation session, or a customer session on a retail website.A user may use a ‘transaction’ command to find transactions based on atransaction definition (e.g., a transaction definition created by theuser ad hoc) or to locate transactions based on a previously configuredtransaction type. In an embodiment, boundaries for transactions may beexplicitly timespan-related, such as, for example, a transactiondefinition that requires that the first and last events in thetransaction be no more than 30 seconds apart and the events within thetransaction be no more than 5 seconds apart. In an embodiment,transaction boundaries may be defined without explicitly setting maximumtimespans or event pauses. For example, a user may define a transactionfor an online store called “item purchase” that looks for groups ofevents that share the same ‘sessionid’ and ‘clientip’ field values, havea starting event with the string “signon”, and an ending event with thestring “purchase.” This exemplary transaction produces grouping ofevents that spans time.

In an embodiment, a tag is a knowledge object that enables a user 1225to efficiently search for events that contain particular field values.In an embodiment, a user 1225 can assign one or more tags to anyfield/value combination, including, for example, event types, hosts,sources, and source types. In an embodiment, tags enable the user 1225to track abstract field values like IP addresses or ID numbers. Forexample, a user 1225 may have a number of field values related to theuser's home office, including an IP address such as 192.168.1.2. In thisexample, the user 1225 may tag these values “homeoffice” and thenperform a search on “tag=homeoffice” to identify the events with fieldvalues that have the homeoffice tag.

In an embodiment, tags enable a user 1225 to group sets of related fieldvalues together. For example, if a user 1225 has two hosts that relateto the same computing device, the user 1225 may assign the two computingdevices the same tag to allow the user 1225 to search on events comingfrom both hosts using the common tag.

In an embodiment, tags enable a user 1225 to assign extracted fieldsmultiple tags that reflect different aspects of their identity.Accordingly, the user 1225 may create tag-based searches that useBoolean operators to narrow down on specific event sets.

In an embodiment, a field extraction is a knowledge object representingboth a process by which fields are extracted from event data and theresults of the extraction process (i.e., extracted fields). In anexample, field extraction may take place either before events areindexed (e.g., in the case of default and indexed fields) or after eventindexing (e.g., in the case of search fields). In an embodiment, a setof default fields may be automatically extracted for each indexed event.In an example, a user 1225 may “create” additional “custom” fields bydefining additional index-time and search-time field extractions. A user1225 may perform manual field extraction through the use of searchcommands, an extraction tool (e.g., interactive field extractorconfigured to enable a user 1225 to create custom fields dynamicallywhile searching), and configuration files. A late-binding schemaincludes such field extractions, as each one may define a particularfield and how to determine a value for the field from the events forwhich the field is defined.

In an embodiment, a transform or field transform is a knowledge objectrepresenting a transformation of an event. For example, a fieldtransform may be used for an advanced type of search-time fieldextraction wherein a user 1225 wants to perform one or more of thefollowing: (i) use the same regular expression across multiple sourcetypes, sources, or hosts; (ii) perform special formatting on theextracted field values; and (iii) extract fields from the values ofanother key field. In an embodiment, a transform may be involved in thesetup of custom index-time field extractions. In an embodiment, a user1225 may create transforms that mask sensitive data in events, such ascustomer credit card numbers. A transform may be involved in thecreation of a lookup, as well as overrides of default host and sourcetype values. In an additional example, a transform may be used to routeevent data to alternate indexes and forward raw event data to athird-party system.

In an embodiment, a lookup is a knowledge object that enables theaddition of fields and related values to search results based on fieldmatching (e.g., using a CSV table or a Python script). For example, auser 1225 may use a lookup to perform DNS or reverse DNS lookups on IPaddresses or host names in the user's data. In an embodiment, a lookupmay be incorporated into a dashboard or other application viewspresented by the system.

In an embodiment, a workflow action is a knowledge object that enablesinteractions between indexed fields in events and other web resources,including external web resources. For example, a workflow action may bedefined to be associated with an IP address field in a user's searchresults or used to perform an external WHOIS lookup based on aparticular value of that field in a specific event. In another example,a workflow action may be defined to use the field values in an HTTPerror event to create a new entry in an external issue tracking system.In a further example, a workflow action may be defined to perform anexternal search (e.g., using a third party search engine or other webapplication) on the value of a field in an event. In another example, aworkflow action may be defined to launch secondary searches that use oneor more field values from selected events.

In an embodiment, a workflow action may be defined that is targeted toevents that contain a particular field or set of fields, or which belongto a specific event type. A workflow action may be defined to openeither in the current window or a new one when selected. In anembodiment, a workflow action may appear in field menus, event menus, orboth (e.g., in search results).

In an embodiment, a search command or command is a knowledge objectrepresenting an element of search language used to interact with datamaintained by the system. The search language may define many commandsthat allow a user 1225 to interact with the system and refine and modifysearch results. Examples of search commands include, but are not limitedto, “stats,” “sort,” and “where”. In an embodiment, a user 1225 mayexpand upon the system's default search language by creating customsearch commands that carry out specialized interactions.

3.2. Configuration Storage Environment

FIG. 13 illustrates an example cluster of search heads 1204 eachincluding a local data store 1330 storing a journal and configurationfile, in accordance with one or more aspects of the present disclosure.In an embodiment, a configuration file (also referred to as a “conffile”) is a file containing system configuration information including,but not limited to, knowledge objects and knowledge objectscustomizations. In an embodiment, a search head 1204 writesconfiguration settings (e.g., knowledge object customizations) into itsrespective configuration file stored in the local data store 1330 of theindividual search head. In such embodiments, each local data store 1330may be local relative to, associated with, or accessed only by itsrespective search head 1204.

In an embodiment, configuration replication is performed to synchronizethe data structures of the multiple configuration files of theindividual search heads across the cluster of search heads such thatchanges (e.g., user-initiated knowledge object customizations) made onone search head are applied on all search heads in the cluster. In anembodiment, a user interface enabling a user to perform search-relatedactivities, submit knowledge object customizations and interact with thesearch heads of the cluster may be provided. Advantageously, knowledgeobject customization and corresponding change or alteration to anindividual configuration file of one search head may be presented to auser via the user interface of another search head in the cluster.

In an embodiment, knowledge object customizations made via the userinterface associated with an individual search head (e.g., a CommandLine Interface (CLI), a Representational State Transfer (REST)application programming interface (API), etc.) are journaled or recordedin a journal maintained in a local data store 1330 of the individualsearch head 1204. In an embodiment, the journal is in-memory or on-diskand stored in the local data store 1330 repository including a historyof knowledge object customizations (e.g., a list of recent configurationcustomization operations) performed by the corresponding search head1204, wherein each knowledge object customization is recorded as ajournal entry. For example, each journal entry includes informationrelating to the knowledge object customization, including, but notlimited to, a knowledge object location (e.g., user/application context,asset type, asset name, etc.), a knowledge object customization type oraction (e.g., creation, modification, move, edit, remove, delete, share,change permissions, etc.), and a knowledge object customization payload(e.g., a key-value pair relating to the creation of a new knowledgeobject customization, a new location relating to a moving of a knowledgeobject customization, a new access control list relating to asharing/permission change).

In an embodiment, a knowledge object customization made by an individualsearch head 1204 is recorded as a journal entry in the journal stored inthe local data store 1330 of the individual search head 1204. Theindividual search head 1204 writes the knowledge object customization toits local configuration file stored in the local data store 1330.

During a synchronization phase including a “pull” stage and a “push”stage, an individual search head communicates with the search headleader, as described in detail below with reference to FIGS. 14-16.During the “pull” stage, the individual search head receives anyknowledge object customization updates from the search head leader.During the “push” stage, the individual search head sends any newjournal entries to the search head leader. In an embodiment, the newjournal entries include any journal entries that have not yet been sentto the search head leader.

As illustrated in FIG. 12, the exemplary cluster 1210 including themultiple associated search heads 1204 is configured in a hub-spokearrangement, wherein one of the multiple search heads 1204 is elected asthe “leader”. In an embodiment, the other search heads 1204 in thecluster engage in intra-cluster communications exclusively with thesearch head leader 1204 (i.e., there is no follower-to-follower searchhead communication). It is to be appreciated that the hub-spokearrangement represents one exemplary topology and other suitabletopologies may be employed in accordance with the present disclosure.

In an embodiment, each search head 1204 may be either in a leader stateor a follower state. In the leader state, the search head leader 1204 isresponsible for synchronizing the knowledge object customizations acrossthe cluster of search heads 1204. In the follower state, each individualsearch heads 1204 receives updates regarding knowledge objectcustomizations performed by other search heads 1204 in the cluster andsends updates regarding knowledge object customizations performed by theindividual search head 1204.

3.3. Configuration Propagation

FIG. 14 presents a flowchart depicting an exemplary method 1400illustrating how an individual search head processes a knowledge objectcustomization in accordance with the disclosed embodiments. At block1410, a search head receives a knowledge object customization from aclient device. In block 1420, the search head generates a new journalentry including information relating to the knowledge objectcustomization and adds the journal entry to a journal stored in a localdata store of the search head. In an embodiment, after adding thejournal entry, the search head may mark the journal entry as “replicatedbut not applied.” In an embodiment, the marking “replicated but notapplied” may represent that the corresponding knowledge objectcustomization has been added to the search head's journal, but not yetadded or applied to the search head's configuration file.

In block 1430, the search head updates a configuration file (stored inthe local data store of the search head) with the knowledge objectcustomization. In an embodiment, the search head updates theconfiguration file by applying the journal entry. For example, if thejournal entry relates to a knowledge object customization involving adeletion of the knowledge object, the search head deletes the respectiveknowledge object from the configuration file. In another example, if thejournal entry is the knowledge object customization involving a creationof the knowledge object, the search head adds the respective knowledgeobject to the configuration file. In yet another example, if the journalentry is the knowledge object customization involving a modification tothe knowledge object, the search head modifies the respective knowledgeobject in the configuration file. In an embodiment, in block 1440, thesearch head may update the corresponding journal entry in the locallystored journal by marking it as “replicated and applied.” Next, if thesearch head is a follower search head, in block 1450, during the “push”stage of the synchronization phase, the search head sends a journalupdate including the journal entry to a search head leader.

In an embodiment, blocks 1420, 1430, and 1440 may be performed in anyorder, such that the activity relating to the journal entry (e.g.,creating the journal entry, updating the journal entry, etc.) may beperformed before, after, or concurrently with the updating of theconfiguration file. For example, in an embodiment, the search head mayfirst update the configuration file with the knowledge objectcustomization. Following or concurrently with the update to theconfiguration file, the search head may generate the new journal entryincluding information relating to the knowledge object customization andadd the journal entry to a journal stored in a local data store of thesearch head. In this embodiment, the search head may record only“complete” changes (i.e., changes that have been applied and reflect inthe configuration file.

In an example implementation of method 1400, a first search head(referred to as “search head 1”) receives instructions relating to aknowledge object customization. In this example, the knowledge objectcustomization involves the creation of a new saved search (referred toas “saved search A”). In an embodiment, search head 1 generates a GUID(“globally unique identifier,” and herein “G1”) identifying the creationof saved search A. Search head 1 then adds a journal entry (herein “J1”)including information relating to G1 (e.g., the knowledge objectcustomization identifier (G1), a parent commit or change associated withthe current journal entry, information identifying the user that createdG1, relevant key-value pairs, etc.) to a journal stored in a local datastore of search head 1. In an embodiment, a parent commit or changerepresents a latest or most recent change received from the search headleader and replicated in a search head's journal. Search head 1 updatesa configuration file stored in the local data store by writing savedsearch A to the configuration file. Next, during the push stage, searchhead 1 sends journal entry J1 to the search head leader.

FIG. 15 presents a flowchart depicting an exemplary method 1500illustrating how a search head leader manages replication of knowledgeobjects across multiple follower search heads in a cluster during asynchronization phase. In block 1510, a search head leader receives ajournal update including a new journal entry (e.g., journal entry J1)relating to a knowledge object customization from a first search head ina cluster. In an embodiment, the search head leader confirms that thereceived journal entry may be “applied cleanly” by confirming that aparent commit or change associated with the journal entry is the latestor most commit in the search head leader's journal. If the receivedjournal entry refers to a parent commit which is the latest commitrecorded in the search head's journal, the search head leader maydetermine that there are no intervening journal updates (i.e., fromother search heads in the cluster) creating a potential merge conflict.In an embodiment, the search head leader “reconciles” the journal entryand determines there is no conflict with another journal entry receivedfrom another follower search head by confirming the parent commit (orjournal entry) reference in the journal entry received from the followersearch head is the latest (e.g., most current) journal entry in thesearch head leader's journal). An example wherein a conflict is detectedamong multiple knowledge object customizations and corresponding journalentries is described below in connection with FIG. 16.

In block 1520, the search head leader adds the one or more journal entryto a local data store of the search head leader. In an embodiment, thesearch head leader may mark the added journal entry as “replicated butnot applied.” In block 1530, the search head leader updates aconfiguration file stored in the local data store of the search headleader with the received journal entry. In an embodiment, in block 1540,following the applying of the knowledge object customizationcorresponding to the received journal entry to its local configurationfile, the search head leader may mark the newly added journal entry as“replicated and applied.” In an embodiment, the search head leader maysend a communication to the follower search head indicating that thefollower search head's “push” was applied successfully.

In an embodiment, blocks 1520, 1530, and 1540 may be performed in anyorder, such that the activity relating to the journal entry (e.g.,creating the journal entry, updating the journal entry, etc.) may beperformed before, after, or concurrently with the updating of theconfiguration file. For example, in an embodiment, the search headleader may first update the configuration file with the knowledge objectcustomization. Following or concurrently with the update to theconfiguration file, the search head may generate the new journal entryincluding information relating to the knowledge object customization andadd the journal entry to a journal stored in a local data store of thesearch head. In this embodiment, the search head leader records“complete” changes (i.e., changes that have been applied and reflect inthe configuration file.

In block 1550, the search head leader synchronizes its journal with theother search heads in the cluster. It is noted that the synchronizationof the search head leader's journal across the multiple follower searchheads of the cluster may be performed at different times or at the sametime. In an embodiment, the synchronization in block 1550 occurs on anindividual basis with respective to the multiple search heads in thecluster. In an embodiment, block 1540 occurs for each individual searchhead upon receipt by the search head leader of a synchronization requestfor the individual search head. For example, the individual search headsmay fetch or “pull” the new changes (e.g., knowledge objectcustomizations) from the search head leader at any time.

FIG. 16 is a flowchart depicting an exemplary method 1600 illustratinghow a search head resolves a conflict relating to the replication ofknowledge object customizations. Continuing the example above, assume asecond search head (herein “search head 2”) of the cluster synchronizeswith the search head leader and receives journal entry J1 from thesearch head leader. The second search head 2 adds journal entry J1 toits local journal and marks it as “replicated but not applied.” Searchhead 2 then updates its local configuration file by applying journalentry J1. In an embodiment, search head 2 then updates its local journalby marking journal entry J1 as “replicated and applied.” At this pointin the example, search head 1 and search head 2 each have journal entryJ1 in their respective local journals, with the latest commit or updatein each local journal being journal entry J1 (relating to G1).

In the example, search head 2 may then generate a new knowledge objectcustomization involving the editing of saved search A. In an embodiment,search head 2 generates a globally unique identifier (herein “G2”)identifying the editing of saved search A. Search head 2 then adds ajournal entry (herein “J2”) including information relating to G2 (e.g.,the knowledge object customization identifier (G2), an identification ofJ1 as a parent commit associated with the current journal entry,information identifying the user that created G2, relevant key-valuepairs, etc.) to a journal stored in a local data store of search head 2.

In addition, in this example, search head 2 may generate a new knowledgeobject customization involving the creation of a new saved search(herein “saved search B”). In an embodiment, search head 2 generates aglobally unique identifier (herein “G3”) identifying the creation ofsaved search B. Search head 2 then adds a journal entry (herein “J3”)including information relating to G3 (e.g., the knowledge objectcustomization identifier (G3), an identification of J2 as a parentcommit associated with the current journal entry, informationidentifying the user that created G3, relevant key-value pairs, etc.) toa journal stored in a local data store of search head 2.

In this example, search head 1 generates a new knowledge objectcustomization involving the editing of saved search A. In an embodiment,search head 1 generates a globally unique identifier (herein “G4”)identifying the editing of saved search A. Search head 1 then adds ajournal entry (herein “J4”) including information relating to G4 (e.g.,the knowledge object customization identifier (G4), an identification ofJ1 as a parent commit associated with the current journal entry,information identifying the user that created G4, relevant key-valuepairs, etc.) to a journal stored in a local data store of search head 1.

In this example, search head 2 synchronizes with the search head leaderbefore search head 1. During the “pull” phase of the synchronizationstage, search head 2 determines that it has all of the search headleader's latest commits. During the “push” phase, search head 2 sendsjournal entries J2 and J3 to the search head leader. The search headleader confirms that J2 and J3 are applied cleanly (i.e., there are noconflicts and the associated knowledge object customizations may beadded to the search head leader's configuration files) and adds J2 andJ3 to its local journal. The search head leader updates its localconfiguration files by applying J2 and J3 and notifies search head 2that the push was successful.

In block 1610, a follower search head (e.g., search head 1 in thisexample) receives one or more journal entries relating to a knowledgeobject customization update from a search head leader during a “pull”stage of a synchronization phase. The search head determines if it hasreceived and journaled all of the received journal entries provided bythe search head leader. For example, when search head 1 engages in the“pull” phase of the synchronization stage, search head 1 determines itdoes not have all of the search head leader commits (i.e., search head 1determines it does not have J2 and J3) and fetches or receives J2 and J3from the search head leader.

With reference to FIG. 16, in block 1620, the search head detects aconflict between the received journal entry and an existing journalentry stored in the local journal of the search head's local data store.In an embodiment, the search head may detect a conflict if the receivedjournal entry and the existing journal entry are sibling journal entries(i.e., the two journal entries share the same parent journal entry). Inanother embodiment, the search head may detect a conflict if thereceived journal entry and the existing journal entry apply to the sameuser or entity. If the existing journal entry has already been applied(i.e., added to the configuration file of the search head leader), thesearch head may proceed to block 1630. In this example, search head 1detects a conflict between the received journal entry (J2) and anexisting journal entry (J4) in its local journal because the two journalentries are both related to a configuration action (e.g., an edit) ofsaved search A.

In block 1630, the search head (e.g., search head 1) resolves thedetected conflict by updating its existing journal entry to produce anupdated journal entry. In an embodiment, the existing journal entry isupdated such that it is effectively applied after the received journalentry by changing the existing journal entry's parent commit reference.In an embodiment, the search head adds the received journal entry to itslocal journal and marks the newly added journal entry as “replicated andapplied.”

In the example above, search head 1 determines that J2 and J4 aresibling commits (i.e., both journal entries have the same parent commit(J1)). Accordingly, search head 1 determines that J2 has already beeneffectively applied. In this case, search head 1 seeks to make J4effectively apply “after” J2. Search head 1 adds J2 to its local journaland marks J2 as “replicated and applied.” In addition, search head 1adds J3 to its local journal and marks J3 as “replicated but notapplied.” Search head 1 then updates its local configuration files byapplying J3 and updates its local journal by marking J3 as “replicatedand applied.” In block 1630, search head 1 resolves the detectedconflict by changing J4's parent commit from J1 to J3 in its localjournal.

Optionally, as shown in FIG. 16, in block 1640, the search head sendsthe updated journal entry to the search head leader. In an embodiment,the “push” phase of the process wherein the search head sends one ormore updated journal entries to the search head leader may be performedfollowing the conflict resolution in block 1630, or at any later timeduring a separate synchronization with the search head. In the exampleabove, search head 1 sends J4 to the search head leader. In anembodiment, the search head leader confirms that J4 applies cleanly. Thesearch head leader adds J4 to its local journal and marks J4 as“replicated but not applied.” The search head leader further updates itslocal configuration files by applying J4 and updates its journal bymarking J4 as “replicated and applied.” In an embodiment, the searchhead leader notifies search head 1 that its push was successful. Next,when search head 2 later synchronizes with the search head leader,search head 2 determines it does not have all of the search head leadercommits and fetches J4.

In an embodiment, search head 2 confirms that J4 applies cleanly (i.e.,that J4's parent commit (J3) is the latest commit in the search headleader's journal). Search head 2 then adds J4 to its local journal andmarks J4 as “replicated but not applied.” Search head 2 updates itslocal configuration files by applying J4 and updates its local journalby marking J4 as “replicated and applied.”

In this stage in the example, search head 1, search head 2 and thesearch head leader each have journal entries J1-J4 in their respectivelocal journals, with the latest commit in each search head's journalbeing J4/G4. In addition, all of the search heads' local configurationfiles contain the “J4 version” of saved search A and the “J3 version” ofsaved search B.

The configuration replication and propagation system is described inmore detail in U.S. patent application Ser. No. 14/448,919, entitled“CONFIGURATION REPLICATION IN A SEARCH HEAD CLUSTER”, filed on 31 Jul.2014, which is hereby incorporated by reference in its entirety for allpurposes.

3.3.1. Search Head Specific Propagation

In one or more embodiments, the search head leader propagates thecustomizations received from the member search heads to other membersearch heads in the cluster as described in FIGS. 14-16. In one or moreembodiments, propagation is performed during the synchronization step(e.g., in response to the “pull” operation initiated by a member searchhead), wherein the search head leader propagates the most recentknowledge object customizations in its journal to other member searchheads. However, propagating all or even a portion of the knowledgeobject customizations in the journal of a search head leader to eachmember search head becomes increasingly inefficient—potentiallyprohibitively so—as the number of knowledge object customizationsscales. In order to reduce the amount of data that is transmitted duringthe synchronization/propagation operations, embodiments of the presentdisclosure provide solutions for determining the divergence between thejournals of each member search head and the journal of the search headleader.

Once the point or origin of divergence is determined for a member searchhead and reported to the search head leader, the search head leader needonly send the knowledge object customizations in its journal after thedivergence point to completely update that particular member searchhead. FIG. 17 presents a flowchart depicting an exemplary method 1700illustrating how knowledge object customization updates are propagatedin a cluster of search heads using divergence determination. Blocks1710-1750 describe exemplary steps comprising the process 1700 depictedin FIG. 17 in accordance with the various embodiments herein described.In one embodiment, the process 1700 is implemented at least in part ascomputer-executable instructions stored in a computer-readable mediumand executed in one or more processing devices.

At block 1710, a first search head in a search cluster accesses a listof knowledge object customizations in a data store. In one or moreembodiments, the first search head is the search head leader and thedata store is a local data store on one or more processing devices uponwhich the search head leader is implemented. In one or more embodiments,the knowledge object customizations from the list of knowledge objectcustomizations corresponds, for example, to one or more of the knowledgeobject customizations described above with respect to FIG. 14-16.

Specifically, each knowledge object customization may correspond to aseparate knowledge object customization submitted by one or more clientdevices, received in one or more member search heads of the searchcluster, and replicated to the first (leader) search head. In one ormore embodiments, rather than the actual knowledge object customizationdata itself (which may be stored in a corresponding configuration file),the list of knowledge object customizations is implemented as a journalof customization records that includes customization details along withcorresponding globally unique identifiers (GUIDs), each GUID beinguniquely associated with (and used to identify) a different and specificknowledge object customization. In one or more embodiments, block 1710may be performed in response to, for example, a “pull” request submittedfrom a second (member) search head in the search cluster.

At block 1730, a digest (or other reduced size representation) of thejournal of the search head leader is generated. In one or moreembodiments, the digest is implemented as a one dimensional array ofvalues (e.g., a bitmask), by applying one or more hash functions to theGUIDs in the local journal of the search head leader. In a specificembodiment, the digest is implemented as a Bloom filter comprising a bitarray of m bits and a pre-determined number k of different defined hashfunctions, each of which maps a GUID to one of the m array positionswith a uniform random distribution. The bits of the Bloom filter areinitially set to 0, and for each GUID in the journal of the search headleader, each of the k hash functions are applied to the GUID to get oneor more corresponding k addresses in the Bloom filter. The bit at eachof the resulting k addresses in the array is thereafter changed to 1,and the presence (or absence) of GUIDs can subsequently be queried aswill be described below. In one or more embodiments, the actual number kof hash functions is proportional to the number of m elements of theBloom filter and adjustable to increase or decrease an intended rate offalse positives of the Bloom filter. Once the Bloom filter has beenpopulated with the hashed values of the GUIDs of the journal, the arrayof bit values (bitmask) is propagated to one or more member search headsat block 1730.

At block 1740, a divergence point between the journal of the search headleader and the journal of a member search head is determined using thebitmask propagated at block 1730 and an indication of the divergencepoint is received in the search head leader. In one or more embodiments,the divergence point is determined to be the earliest point ofdivergence (i.e., where the journals of the search head leader and themember search head are no longer synchronous), and the indication of thedivergence point may consist of, for example, an address in the bitmaskof the Bloom filter corresponding to the most recent GUID prior to thepoint of divergence, a corresponding position of the GUID in the localjournal of the search head leader, the hash values of the GUID, or theGUID itself.

The search head leader then determines the position in its journalcorresponding to the indication of the divergence point received atblock 1740, and sends the knowledge object customizations in the localjournal of the search head leader that was recorded subsequent(chronologically) to the point of divergence to the member search headat block 1750. Since the portions of the respective journals of thesearch head leader and member search head were synchronized before thedivergence point, communicating the knowledge object customizationsprior to the divergence point would be redundant, and the search headleader needs only to propagate the knowledge object customizations whichwere recorded in the journal of the search head leader after thedivergence point to synchronize with the journal of the member searchhead. By sending only the knowledge object customizations after thepoint of divergence, the propagation of configuration data is improvedby transmitting a portion of data customized for the particular membersearch head, thereby eliminating the transmission of redundant knowledgeobject customization data and significantly reducing the amount of datatransferred overall.

FIG. 18 presents a flow diagram 1800 illustrating the interaction ofcomponents in a search cluster during a determination of a divergencepoint, in accordance with the disclosed embodiments. Specifically, FIG.18 depicts the components involved and the chronological flow of dataduring a specific embodiment of the performance of steps 1710-1750 in aprocess for communicating configuration customization updates from asearch head leader, as described above with respect to FIG. 17. Aspresented in FIG. 18, one or more hash functions (f(hash)) are appliedto the local journal 1330 relative to a search head leader 1204 togenerate a Bloom Filter at Time A. A bitmask (bit array) representingthe Bloom Filter is propagated to the journal 1204 of a member searchhead 1204 at Time B. The same hash functions (f(hash)) are then appliedto the journal of the member search head and the addresses derived arecompared to the Bloom Filter at Time C until an element (e.g., a GUID)in the journal of the member search head is determined to be missingfrom the addresses in the Bloom Filter derived after applying the one ormore hash functions to the GUID. This point p of divergence (i.e.,asynchronicity) is then communicated to the search head leader at TimeE, and the knowledge object customizations of the journal local to thesearch head leader that were recorded relative (i.e., subsequent) to theposition in the journal corresponding to point p are propagated to themember search head at Time E. In one or more embodiments, the same flowof data is performed by the search head leader in combination with eachof member search head in the search cluster continuously atpre-determined intervals, according to a pre-determined order, orsubject to a specified trigger event.

Embodiments of the present disclosure have been described with referenceto digests and other reduced-size representations generated as or usingBloom filters. Relative to other filtering techniques and datastructures, Bloom filters are relatively space and time efficient togenerate and maintain. As such, to continue addressing processingrequirements as they scale, a new Bloom filter may be generated when theoptimal filtering capacity of previous Bloom filter(s) has beenexhausted to maximize the information content (i.e., storing as manyGUIDs as possible) while maintaining an acceptable false positive rate.

FIG. 19 presents a flowchart illustrating how a size of a local list ofknowledge configuration customizations in the search head leader ismanaged, in accordance with the disclosed embodiments. Blocks 1910-1940describe exemplary steps comprising the process 1900 depicted in FIG. 19in accordance with the various embodiments herein described. In oneembodiment, the process 1900 is implemented at least in part ascomputer-executable instructions stored in a computer-readable mediumand executed in one or more processing devices.

At block 1910, an update list of knowledge object customizations isreceived in a search head leader. In one or more embodiments, the updatelist of knowledge object customizations may generated locally in thesearch head leader from a remote client device, or alternate, can besent from a member search head during a “push” operation of asynchronization phase, as described above with respect to FIGS. 14-16.The update list may, for example, consist of knowledge objectcustomizations submitted from a client computing device to the membersearch head that has not been recorded in the search head leader orpropagated to other member search heads of the cluster.

At block 1920, a current size of the digest (e.g., Bloom filter) iscompared to a threshold. In one or more embodiments, the size of thedigest is measured as the number of the current population of elements(e.g., GUIDs) in the digest or a remaining capacity. In one or moreembodiments, the threshold is a pre-determined threshold thatcorresponds to a (predetermined) intended false positive rate. Thus,exceeding the threshold has the potential to cause the Bloom filter toproduce false positives at a rate that is higher thanintended/acceptable. If the current size of the digest is less than thethreshold (i.e., the remaining capacity is greater than a capacitythreshold), the GUIDs of the knowledge object customizations from theupdate list are added to the digest at block 1930. In one or moreembodiments, adding the GUIDs may be performed by adding the GUIDs tothe Bloom filter by hashing the GUID values as described above. In oneor more embodiments, block 1920 is performed by comparing the size ofthe digest including the size of the update list or remaining capacityof the digest after the addition of the update list) with thepre-determined threshold. In one or more embodiments, the threshold maycomprise a range of sizes.

If the current size of the digest is determined at block 1920 to begreater than the size threshold (or if the remaining capacity of thedigest is determined to be less than a capacity threshold), the processproceeds to block 1940, whereupon a second digest is generated. Insubsequent interactions, propagation of knowledge object customizationsperformed by the search head leader across search heads in the clustermay be performed by sending both the first digest and second digest tomember search heads to determine a point of divergence.

Alternately, the search head leader may send only the second digest ifdivergence points have been determined to correspond to GUIDs within thesecond digest. In one or more embodiments, the second digest may have ainitial size substantially equivalent to the initial size of the firstdigest. That is, the second digest may be implemented as a second bitarray having an equal or substantially equal length to the first bitarray of the first digest. In alternate embodiments, the second digestmay have a variable size depending on scaling patterns and behaviors.

3.3.2. Divergence Determination

Once a search head leader sends a digest of the local journal of thesearch head leader to a member search head, the point of divergencebetween the journal of the search head leader and the local journal ofthe member search head is determined. In one or more embodiments, thepoint of divergence is determined by querying the presence or absence ofthe GUIDs in the journal of the member search head in the digest.According to specific embodiments, the potential presence or thedefinite absence of a GUID can be determined by implementing the digestas a Bloom filter, hashing the GUIDs in the journal of the member searchhead to determine addresses in the bitmask array of the Bloom filter,and referencing the address values (bit values) of the bitmask at theaddresses generated from the application of the hash functions. In oneor more embodiments, the presence/absence of the GUIDs in the journal ofthe member search head can be performed iteratively.

FIG. 20 presents a flowchart illustrating how to iteratively identify adivergence point between the journal of a member search head and thejournal of a search head leader, in accordance with the disclosedembodiments. Blocks 2010-2050 describe exemplary steps comprising theprocess 2000 depicted in FIG. 20 in accordance with the variousembodiments herein described. In one embodiment, the process 2000 isimplemented at least in part as computer-executable instructions storedin a computer-readable medium and executed in one or more processingdevices.

At block 2010, one or more hash functions are applied by a member searchhead to a GUID in the local journal of the member search head todetermine a corresponding one or more hash values. In one or moreembodiments, the one or more hash functions correspond to the same oneor more hash functions used to generate the digest (e.g., a Bloomfilter) in the search head leader. According to various embodiments, theone or more hash functions may be pre-determined, previouslycommunicated, or sent to the member search head along with the digest.

In one or more embodiments, the hash values correspond to addresses inan array (bitmask) representing the digest. At block 2020, the bitvalues at the addresses determined at block 2010 in the bitmask arereferenced. The determination of the (potential) presence or absence ofthe GUID is performed at block 2030. The presence or absence of the GUIDmay be determined according to the following: 1) if any of the bits atresulting addresses in the bitmask is 0, the GUID is determined to bedefinitely not in the digest; 2) if all of the bits at the resultingaddresses are determined to be 1, then either the GUID is in the set orthe bits of the bitmask were set to 1 during the insertion of otherGUIDs, and the result is a false positive.

If a GUID is determined to definitely not be in the digest by theperformance of blocks 2020 and 2030, the position of the GUID in thelocal journal of the member search head is identified as the point ofdivergence and the information is relayed to the search head leader atblock 2040. Such information may comprise, for example, information thatidentifies the GUID immediately preceding the divergence point (i.e.,the last shared GUID between the respective journals of the search headleader and member search head). Subsequently, the search head leaderdetermines the position in its local journal corresponding to the lastshared GUID, and sends all subsequent knowledge object customizationdata (e.g., GUIDs and configuration data) to the member search head.Once received, the knowledge object customization data is used to updatethe journal and configuration file in the member search head, and themember search head is again synchronized with the search head leader.

If instead the GUID is determined to potentially be present in thedigest through the performance of blocks 2020 and 2030, the processproceeds to block 2050, where the next GUID in the local journal isretrieved, and blocks 2010 through 2050 are repeated. In one or moreembodiments, the sequence of blocks 2010 through 2050 can be repeatediteratively for each GUID in the local journal of the member search headuntil the divergence point is determined (i.e., until a GUID isdetermined to be absent). Additionally, blocks 2010-2050 can beperformed for all member search heads in the cluster, with each membersearch head determining a respective divergence point, communicating thedivergence point to the search head leader, receiving a customizedupdate list of knowledge object customizations specific to the membersearch head, and updating a local journal or configuration file with theinformation in the update list. In one or more embodiments, theperformance of steps 2010 to 2050 can be performed for each membersearch head periodically at pre-determined intervals or in response to atrigger event. In still further embodiments the order in which process2000 is performed among the member search heads follows a cyclical(e.g., round robin) format.

3.3.3. False-Positive Reduction Techniques

An inherent characteristic of Bloom filters is the potential for falsepositives in response to search queries. A false positive is undesirablewhen using Bloom filters to synchronize knowledge object customizationsin a search head cluster since the location of a divergence point may beinaccurately determined to be later than the actual point of divergence,which may prevent the search head leader from propagating everyknowledge object customization necessary to ensure synchronization.Embodiments of the present disclosure provide techniques for reducingthe rate of false positives for embodiments of the present disclosurethat use Bloom filters while still maintaining or maximizing theinformation content in a digest (or other reduced-size representation).

FIG. 21 presents a flowchart illustrating how to iteratively identify adivergence point between the journal of a member search head and thejournal of a search head leader with a dynamic window of contiguousknowledge object customizations, in accordance with the disclosedembodiments. Blocks 2110-2170 describe exemplary steps comprising theprocess 2100 depicted in FIG. 21 in accordance with the variousembodiments herein described. In one embodiment, the process 2100 isimplemented at least in part as computer-executable instructions storedin a computer-readable medium and executed in one or more processingdevices.

In one or more embodiments, reducing the false positive rate can beperformed by incorporating in the comparison between journals acontiguous sequence of GUIDs in each journal to determine a divergencepoint, rather than considering each GUID individually, since theprobability of consecutive false positives over a contiguous sequence ofqueries is substantially less than the discrete probability of anysingle false positive result, and false positive results. Blocks2110-2150 are similar to blocks 2010-2050 described above with respectto FIG. 20, but applied to all GUIDs in a set of GUIDs. Thus, at block2110, the set of one or more hash functions are applied to each GUID inthe set of GUIDs, and corresponding one or more hash values (addresses)for each GUID in the set is determined.

At block 2120, the addresses in the digest corresponding to the hashvalues determined at block 2110 are referenced, and the presence of allGUIDs in the entire set is determined based on the bit values at theaddresses in the digest. If any GUID in the set is determined to beabsent from the digest, this indicates the presence of a point ofdivergence in that set of GUIDs, and the set of GUIDs is then comparedin block 2140 to the corresponding sequence of elements in the list ofknowledge object customizations in the search head leader to identifythe point of divergence, and if no GUIDs in the set are determined to beabsent, the next GUID in the journal is received, with process 2100being repeated iteratively for each GUID in the local journal of themember search head.

In one or more embodiments, the set of GUIDs being considered in any onequery can be implemented as having a constant size during thedetermination of a divergence point. According to these specificembodiments, once the next GUID in the journal is received (by referenceto the local journal, for example) the first (earliest) GUID in the setof GUIDs is removed from the set at block 2160 and the next GUID in thejournal (received in block 2150) is appended to the end of the set ofGUIDs, and blocks 2110 through 2170 are iteratively repeated for eachGUID remaining in the local journal of the member search head until aGUID is determined to be absent.

In this manner, a contiguous, moving “window” or block of GUIDs of aconstant size is maintained and used to evaluate and identify thedivergence point. In one or more embodiments, to populate the initialset of GUIDs, each GUID can be iteratively added to the set (with theentire set being iteratively evaluated) until the set reaches thepre-determined size. Since each GUID in the set has been previouslyevaluated and determined to be present in the digest (with a reducedprobability of a false positive), the assurance the first absent GUIDbeing the point of divergence is high.

As with blocks 2010 through 2050 of process 2000 described above, blocks2110-2170 can be performed for all member search heads in the cluster,with each member search head determining a respective divergence point,communicating the divergence point to the search head leader, receivinga customized update list of knowledge object customizations specific tothe member search head, and updating a local journal or configurationfile with the information in the update list. In one or moreembodiments, the performance of steps 2110 to 2170 can be performed foreach member search head periodically at pre-determined intervals or inresponse to a trigger event. In still further embodiments the order inwhich process 2100 is performed among the member search heads follows acyclical (e.g., round robin) format.

What is claimed is:
 1. A method comprising: accessing, by one or moreprocessing devices corresponding to a first search head of a clustercomprising a plurality of search heads of a data aggregation andanalysis system, a first list of knowledge object customizationscorresponding to the first search head; generating a digest of the firstlist of knowledge object customizations corresponding to the firstsearch head; propagating the digest to a second search head of thecluster; receiving an indication of a first divergence point determinedbetween the first list of knowledge object customizations and a secondlist of knowledge object customizations corresponding to the secondsearch head based on a comparison of the digest with the second list ofknowledge object customizations, the first divergence pointcorresponding to a first position in the first list of knowledge objectcustomizations; and sending a first set of knowledge objectcustomizations from the first list of knowledge object customizations tothe second search head, wherein the first set of knowledge objectcustomizations comprises at least one knowledge object customizationfrom the first list of knowledge object customizations relative to aposition in the first list of knowledge object of the first divergencepoint.
 2. The method of claim 1, wherein the first list of knowledgeobject customizations comprises a journal of knowledge objectcustomizations locally stored with respect to at least one processingdevice of the one or more processing devices corresponding to the firstsearch head.
 3. The method of claim 1, wherein the second list ofknowledge object customizations corresponding to the second search headcomprises a journal of knowledge object customizations locally storedwith respect to at least one processing device of one or more processingdevices corresponding to the second search head.
 4. The method of claim1, wherein the generating the digest comprises applying a Bloom filterto the first list of knowledge object customizations.
 5. The method ofclaim 1, wherein the first list of knowledge object customizationscomprises a list of global unique identifiers (GUIDs), each knowledgeobject customization of the first list of knowledge objectcustomizations corresponding specifically to a respective GUID of thelist of GUIDs.
 6. The method of claim 1, wherein the digest comprises abitmask generated by applying a Bloom filter to a list of global uniqueidentifiers (GUID) values corresponding to the first list of knowledgeobject customizations.
 7. The method of claim 1, wherein the sending thefirst set of knowledge object customizations comprises: sending a firstset of knowledge object customizations comprising at least one knowledgeobject customization positioned in the first list of knowledge objectcustomizations relative to the first divergence point to the secondsearch head; and updating, in the second search head, the second list ofknowledge object customizations with the first set of knowledge objectcustomizations.
 8. The method of claim 1, further comprising:propagating the digest to a third search head of the cluster; receivingan indication of a second divergence point determined between the digestand a third list of knowledge object customizations corresponding to thethird search head, the second divergence point corresponding to a secondposition in the digest; and sending a second set of knowledge objectcustomizations from the first list of knowledge object customizations tothe third search head, wherein the second set of knowledge objectcustomizations comprises knowledge object customizations from the firstlist of configurations after a position in the first list ofconfigurations corresponding to the second divergence point.
 9. Themethod of claim 1, wherein the generating the digest comprises:receiving an update list of knowledge object customizations from thesecond search head, the update list comprising at least one knowledgeobject customization from the second list of knowledge objectcustomizations absent from the digest; comparing a current size of thedigest with a pre-determined threshold size; updating the digest and thefirst list of knowledge object customizations to include the knowledgeobject customizations comprised in the update list when the current sizeof the digest is less than a pre-determined threshold size; andgenerating a second digest based on the update list when the size of thedigest meets or exceeds the pre-determined threshold size.
 10. Themethod of claim 1, wherein the first divergence point is determined by:applying one or more hash functions to a first GUID corresponding to afirst knowledge object customization of the second list of knowledgeobject customizations to determine a corresponding one or more hashvalues; referencing one or more addresses in the digest corresponding tothe one or more hash values to determine one or more correspondingaddress values, wherein the one or more address values are indicative ofthe presence or absence of the first GUID in the digest; determining apresence or absence of the first GUID in the digest based on the one ormore address values; and calculating the divergence point to be aposition in the second list of knowledge object customizationscorresponding to a position of the first GUID if the first GUID isabsent from the digest.
 11. The method of claim 1, wherein the firstdivergence point is determined by: applying, to a set of GUIDscorresponding to a pre-determined number of knowledge objectcustomizations of the second list of knowledge object customizations,one or more hash functions to each of the set of GUIDs to determine acorresponding one or more hash values for each GUID of the set of GUIDs;referencing one or more addresses in the digest corresponding to the oneor more hash values to determine a corresponding one or more addressvalues for each GUID of the set of GUIDs, wherein the one or moreaddress values is indicative of the presence or absence of thecorresponding GUID in the digest; determining a presence of each GUID ofthe set of GUIDs in the digest based on the one or more address values;iteratively repeating the applying, the referencing, and the determiningfor remaining GUIDs in the set of GUIDs until an absence of a GUID inthe digest is determined; and when a GUID is determined to be absent inthe digest, identifying the divergence point as a position in the secondlist of knowledge object customizations corresponding to the GUID of theset of GUIDs that is absent from the digest.
 12. The method of claim 1,wherein the first divergence point is determined by: applying, to a setof GUIDs comprising a sequence of consecutive GUIDs corresponding to apre-determined number of knowledge object customizations of the secondlist of knowledge object customizations, one or more hash functions toeach of the set of GUIDs to determine a corresponding one or more hashvalues for each GUID of the set of GUIDs; referencing one or moreaddresses in the digest corresponding to the one or more hash values todetermine a corresponding one or more address values, wherein the one ormore address values is indicative of the presence or absence of thecorresponding GUID in the digest; determining a presence of each GUID ofthe set of GUIDs in the digest based on the one or more address values;iteratively repeating the applying, the referencing, and the determiningfor remaining GUIDs in the set of GUIDs until an absence of a GUID inthe digest is determined; and when a GUID is determined to be absent inthe digest, comparing the set of GUIDs to a corresponding sequence ofGUIDs in the first list of knowledge object customizations to identifythe divergence point, wherein the iteratively repeating comprises, foreach iteration, adding a GUID corresponding to a next knowledge objectcustomization of the second list of knowledge object customizations tothe set of GUIDs and performing the iteratively repeating for thesequence of consecutive GUIDs.
 13. The method of claim 1, wherein thefirst divergence point is determined by: applying, to a set of GUIDscomprising a sequence of consecutive GUIDs corresponding to apre-determined number of knowledge object customizations of the secondlist of knowledge object customizations, one or more hash functions toeach of the set of GUIDs to determine a corresponding one or more hashvalues for each GUID of the set of GUIDs; referencing one or moreaddresses in the digest corresponding to the one or more hash values todetermine a corresponding one or more address values, wherein the one ormore address values is indicative of the presence or absence of thecorresponding GUID in the digest; determining a presence of each GUID ofthe set of GUIDs in the digest based on the one or more address values;removing the first GUID in the set of GUIDS and appending the set ofGUIDs with a GUID corresponding to a next knowledge object customizationof the second list of knowledge object customizations when all GUIDS inthe set of GUIDs are determined to be present in the digest; iterativelyrepeating the applying, the referencing, and the determining forremaining GUIDs in the one or more GUIDs until an absence of a GUID inthe digest is determined; and when a GUID is determined to be absent inthe digest, comparing the set of GUIDs to a corresponding sequence ofGUIDs in the first list of knowledge object customizations to identifythe divergence point.
 14. The method of claim 1, wherein the firstdivergence point is determined by: applying, to a set of GUIDscomprising a sequence of consecutive GUIDs corresponding to apre-determined number of knowledge object customizations of the secondlist of knowledge object customizations, one or more hash functions toeach of the set of GUIDs to determine a corresponding one or more hashvalues for each GUID of the set of GUIDs; referencing one or moreaddresses in the digest corresponding to the one or more hash values todetermine a corresponding one or more address values, wherein the one ormore address values is indicative of the presence or absence of thecorresponding GUID in the digest; determining a presence of all GUIDs ofthe set of GUIDs in the digest based on the one or more address values;removing the first GUID in the set of GUIDs and appending the set ofGUIDs with a GUID corresponding to a next knowledge object customizationof the second list of knowledge object customizations when all GUIDS inthe set of GUIDs are present in the digest; iteratively repeating theapplying, the referencing, and the determining for remaining GUIDs inthe one or more GUIDs until an absence of a GUID in the digest isdetermined; and when a GUID is determined to be absent in the digest,comparing the set of GUIDs to a corresponding sequence of GUIDs in thefirst list of knowledge object customizations to identify the divergencepoint, wherein a size of the set of GUIDs is configurable.
 15. Themethod of claim 1, wherein the digest comprises a one dimensional arrayof values, the one dimensional array having a configurable size.
 16. Themethod of claim 1, wherein the data aggregation and analysis systememploys a late-binding schema for searching data.
 17. A systemcomprising: a local data store comprising a first list of knowledgeobject customizations received from at least one client computingdevice; and a processing device coupled with the local data store, theprocessing device being configured to: access, by a first search head ofa cluster comprising a plurality of search heads, the first list ofknowledge object customizations from the local data store; generate adigest of the first list of knowledge object customizationscorresponding to the first search head, the digest being stored in thelocal data store; propagate the digest to a second search head of thecluster; and receive an indication of a first divergence pointdetermined between the first list of knowledge object customizations anda second list of knowledge object customizations corresponding to thesecond search head based on a comparison of the digest and the secondlist of knowledge object customizations, the first divergence pointcorresponding to a first position in the first list of knowledge objectcustomizations; and send a first set of knowledge object customizationsfrom the first list of knowledge object customizations to the secondsearch head, wherein the first set of knowledge object customizationscomprises at least one knowledge object customization from the firstlist of knowledge object customizations relative to a position in thefirst list of knowledge object customizations corresponding to the firstdivergence point.
 18. The system of claim 17, wherein the first list ofknowledge object customizations corresponding to the first search headcomprises a journal of knowledge object customizations stored in thelocal data store corresponding to the first search head.
 19. The systemof claim 17, wherein the processing device is configured to generate thedigest of the first list of knowledge object customizations by applyinga Bloom filter to the first list of knowledge object customizations inthe local data store.
 20. The system of claim 17, wherein the first listof knowledge object customizations comprises a list of global uniqueidentifiers (GUIDs), each knowledge object customization of the firstlist of knowledge object customizations corresponding specifically to arespective GUID of the list of GUIDs.
 21. The system of claim 17,wherein the digest comprises a bitmask generated by the processingdevice by applying a Bloom filter to a list of global unique identifiers(GUID) values corresponding to the first list of knowledge objectcustomizations.
 22. The system of claim 17, wherein the processor isconfigured to send the first set of knowledge object customizations bysending at least one knowledge object customization positioned in thefirst list of knowledge object customizations after the first divergencepoint to the second search head, wherein the second list of knowledgeobject customizations is updated in the second search head based on thefirst set of knowledge object customizations.
 23. The system of claim17, wherein the processing device is further configured to: propagatethe digest to a third search head of the cluster; receive an indicationof a second divergence point determined between the digest and a thirdlist of knowledge object customizations corresponding to the thirdsearch head, the second divergence point corresponding to a secondposition in the digest; and send a second set of knowledge objectcustomizations from the first list of knowledge object customizations tothe third search head, wherein the second set of knowledge objectcustomizations comprises knowledge object customizations from the firstlist of configurations after a position in the first list ofconfigurations corresponding to the second divergence point.
 24. Thesystem of claim 17, wherein the processing device is further configuredto: receive an update list of knowledge object customizations from thesecond search head, the update list comprising at least one knowledgeobject customization from the second list of knowledge objectcustomizations absent from the digest; compare a current size of thedigest with a pre-determined threshold size; update the digest and thefirst list of knowledge object customizations to include the knowledgeobject customizations comprised in the update list when the current sizeof the digest is less than a pre-determined threshold size; and generatea second digest based on the update list when the size of the digestmeets or exceeds the pre-determined threshold size.
 25. The system ofclaim 17, wherein the first divergence point is determined by: applying,to a set of GUIDs corresponding to a pre-determined number of knowledgeobject customizations of the second list of knowledge objectcustomizations, one or more hash functions to each of the set of GUIDsto determine a corresponding one or more hash values for each GUID ofthe set of GUIDs; referencing one or more addresses in the digestcorresponding to the one or more hash values to determine acorresponding one or more address values, wherein the one or moreaddress values is indicative of the presence or absence of thecorresponding GUID in the digest; determining a presence or absence ofeach GUID of the set of GUIDs in the digest based on the one or moreaddress values; iteratively repeating the applying, the referencing, andthe determining for remaining GUIDs in the set of GUIDs until an absenceof a GUID in the digest is determined; and when a GUID is determined tobe absent in the digest, identifying the divergence point as a positionin the second list of knowledge object customizations corresponding tothe GUID of the set of GUIDs that is absent from the digest.
 26. Thesystem of claim 17, wherein the first divergence point is determined by:applying, to a set of GUIDs corresponding to a pre-determined number ofknowledge object customizations of the second list of knowledge objectcustomizations, one or more hash functions to each of the set of GUIDsto determine a corresponding one or more hash values for each GUID ofthe set of GUIDs; referencing one or more addresses in the digestcorresponding to the one or more hash values to determine acorresponding one or more address values, wherein the one or moreaddress values is indicative of the presence or absence of thecorresponding GUID in the digest; determining a presence or absence ofeach GUID of the set of GUIDs in the digest based on the one or moreaddress values; iteratively repeating the applying, the referencing, andthe determining for remaining GUIDs in the set of GUIDs until an absenceof a GUID in the digest is determined; and when a GUID is determined tobe absent in the digest, comparing the set of GUIDs to a correspondingsequence of GUIDs in the first list of knowledge object customizationsto identify the divergence point, wherein the iteratively repeatingcomprises, for each iteration, adding a GUID corresponding to a nextknowledge object customization of the second list of knowledge objectcustomizations to the set of GUIDs and performing the iterativelyrepeating for the sequence of consecutive GUIDs.
 27. The system of claim17, wherein the first divergence point is determined by: applying, to aset of GUIDs corresponding to a pre-determined number of knowledgeobject customizations of the second list of knowledge objectcustomizations, one or more hash functions to each of the set of GUIDsto determine a corresponding one or more hash values for each GUID ofthe set of GUIDs; referencing one or more addresses in the digestcorresponding to the one or more hash values to determine acorresponding one or more address values, wherein the one or moreaddress values is indicative of the presence or absence of thecorresponding GUID in the digest; determining a presence or absence ofall GUIDs of the set of GUIDs in the digest based on the one or moreaddress values; removing the first GUID in the set of GUIDS andappending the set of GUIDs with a GUID corresponding to a next knowledgeobject customization of the second list of knowledge objectcustomizations when all GUIDS in the set of GUIDs are determined to bepresent in the digest; iteratively repeating the applying, thereferencing, and the determining for remaining GUIDs in the one or moreGUIDs until an absence of a GUID in the digest is determined; and when aGUID is determined to be absent in the digest, comparing the set ofGUIDs to a corresponding sequence of GUIDs in the first list ofknowledge object customizations to identify the divergence point.
 28. Anon-transitory computer readable medium having instructions storedthereon which, when executed by a processing device, causes theprocessing device to perform configuration propagation, the instructionscomprising: instructions to access, by one or more processing devicescorresponding to a first search head of a cluster comprising a pluralityof search heads of a data aggregation and analysis system, a first listof knowledge object customizations corresponding to the first searchhead; instructions to generate a digest of the first list of knowledgeobject customizations corresponding to the first search head;instructions to propagate the digest to a second search head of thecluster; instructions to receive an indication of a first divergencepoint determined between the first list of knowledge objectcustomizations and a second list of knowledge object customizationscorresponding to the second search head based on a comparison of thedigest and the second list of knowledge object customizations, the firstdivergence point corresponding to a first position in the first list ofknowledge object customizations; and instructions to send a first set ofknowledge object customizations from the first list of knowledge objectcustomizations to the second search head, wherein the first set ofknowledge object customizations comprises at least one knowledge objectcustomization from the first list of knowledge object customizationsrelative to a position in the first list of knowledge objectcustomizations corresponding to the first divergence point.
 29. Thenon-transitory computer readable medium of claim 28, further comprising:instructions to receive an update list of knowledge objectcustomizations from the second search head, the update list comprisingat least one knowledge object customization from the second list ofknowledge object customizations absent from the digest; instructions tocompare a current size of the digest with a pre-determined thresholdsize; instructions to update the digest and the first list of knowledgeobject customizations to include the knowledge object customizationscomprised in the update list when the current size of the digest is lessthan a pre-determined threshold size; and instructions to generate asecond digest based on the update list when the size of the digest meetsor exceeds the pre-determined threshold size.
 30. The non-transitorycomputer readable medium of claim 28, further comprising: instructionsto apply, to a set of GUIDs corresponding to a pre-determined number ofknowledge object customizations of the second list of knowledge objectcustomizations, one or more hash functions to each of the set of GUIDsto determine a corresponding one or more hash values for each GUID ofthe set of GUIDs; instructions to reference one or more addresses in thedigest corresponding to the one or more hash values to determine acorresponding one or more address values, wherein the one or moreaddress values is indicative of the presence or absence of thecorresponding GUID in the digest; instructions to determine a presenceor absence of all GUIDs of the set of GUIDs in the digest based on theone or more address values; instructions to iteratively repeat theapplying, the referencing, and the determining for remaining GUIDs inthe set of GUIDs until an absence of a GUID in the digest is determined;and instructions to compare, when a GUID is determined to be absent inthe digest, the set of GUIDs to a corresponding sequence of GUIDs in thefirst list of knowledge object customizations to identify the divergencepoint, wherein the instructions to iteratively repeat comprises,instructions to add, for each iteration, a GUID corresponding to a nextknowledge object customization of the second list of knowledge objectcustomizations to the set of GUIDs and instructions to perform theiteratively repeating for the sequence of consecutive GUIDs.